PhD Chapter 3

Results 2/3


This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.4.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Spatial variation of exposure indices

Here, we compute semivariograms for each exposure index (on the whole raster, not only extracted values at the stations).

Aquaculture
## Model selected: Lin
## nugget = 0; sill = 0.00426; range = 1.66735; kappa = 0.5

City
## Model selected: Exp
## nugget = 0.00038; sill = 0.00915; range = 11.75343; kappa = 0.5

Sediment dredging
## Model selected: Sph
## nugget = 0; sill = 0.01506; range = 1.84013; kappa = 0.5

Industry
## Model selected: Sph
## nugget = 1e-04; sill = 0.00793; range = 7.91521; kappa = 0.5

Sewers
## Model selected: Lin
## nugget = 0; sill = 0.00674; range = 4.7487; kappa = 0.5

Shipping
## Model selected: Lin
## nugget = 0; sill = 0.08063; range = 3.08768; kappa = 0.5

Fisheries
## Model selected: Lin
## nugget = 0; sill = 0.026; range = 3.51963; kappa = 0.5

2. Relationships with abiotic parameters

2.1. Covariation

Several types of models were considered to explore relationships: linear, quadratic, exponential and logarithmic. The model with the highest \(R^{2}\) is presented on each plot.

⚠️ Only linear models were implemented for now, as there are some bugs with the calculation of the others.

Aquaculture

City

Sediment dredging

Industry

Sewers

Shipping

Fisheries

Cumulative exposure

2.2. Correlation

Correlations have been calculated with Spearman’s rank coefficient.

Correlation coefficients between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture -0.157 0.09 0.061 -0.046 0.149 -0.29 -0.28 -0.337 -0.398 -0.374 -0.343 -0.282 -0.304 -0.392 0.214 0.118 0.044 0.156 -0.018
city -0.245 0.018 0.482 -0.344 -0.109 -0.381 -0.297 -0.252 -0.002 -0.035 -0.221 -0.311 -0.277 -0.128 -0.065 0.013 -0.043 -0.08 -0.058
dredging 0.24 -0.128 -0.084 0.152 -0.007 0.13 -0.016 0.134 0.215 0.441 0.239 -0.022 0.08 0.165 -0.207 -0.23 0.023 -0.044 0.091
industry 0.181 -0.085 -0.04 0.059 0.085 0.126 0.072 0.327 0.486 0.573 0.497 0.123 0.2 0.357 -0.202 -0.129 0.056 -0.124 -0.025
sewers 0.118 0.096 -0.297 0.197 0.38 0.459 0.346 0.433 0.399 0.364 0.478 0.317 0.476 0.401 -0.288 -0.088 0.094 -0.274 -0.076
shipping 0.499 -0.179 -0.465 0.458 -0.09 0.613 0.615 0.558 0.52 0.424 0.545 0.694 0.614 0.599 -0.038 0.023 0.035 -0.049 -0.066
fisheries -0.501 0.2 0.387 -0.387 -0.139 -0.571 -0.534 -0.556 -0.611 -0.583 -0.588 -0.549 -0.57 -0.615 0.311 0.185 -0.07 0.218 -0.026
cumulative_exposure 0.274 -0.03 -0.253 0.209 0.084 0.396 0.289 0.385 0.459 0.492 0.469 0.327 0.413 0.458 -0.029 -0.043 0.004 -0.059 -0.095
p-values of correlation test between exposure indices and ecosystem variables
  om gravel sand silt clay arsenic cadmium chromium copper iron manganese mercury lead zinc S N B H J
aquaculture 0.1049 0.356 0.5302 0.637 0.1237 0.002355 0.003387 0.000367 2.018e-05 6.633e-05 0.0002733 0.003079 0.001372 2.658e-05 0.02649 0.2242 0.6522 0.1065 0.8544
city 0.01052 0.8539 1.293e-07 0.0002621 0.2603 4.76e-05 0.001777 0.008575 0.9846 0.7191 0.02172 0.001066 0.003668 0.1851 0.5041 0.8965 0.6607 0.4117 0.5532
dredging 0.01246 0.1881 0.3869 0.1168 0.9444 0.1786 0.8735 0.1674 0.02561 1.83e-06 0.01273 0.8227 0.4114 0.08719 0.03162 0.01677 0.8104 0.6548 0.348
industry 0.0607 0.3835 0.684 0.5448 0.3823 0.1941 0.4572 0.000558 9.69e-08 8.902e-11 4.614e-08 0.2063 0.03824 0.0001519 0.03569 0.1829 0.5615 0.2021 0.7935
sewers 0.225 0.3223 0.001809 0.04071 4.884e-05 6.031e-07 0.0002411 2.869e-06 1.881e-05 0.0001091 1.684e-07 0.0008276 1.881e-07 1.714e-05 0.00247 0.3676 0.3327 0.004064 0.4336
shipping 3.827e-08 0.0642 3.855e-07 6.054e-07 0.3517 1.692e-12 1.394e-12 3.633e-10 7.839e-09 4.836e-06 1.09e-09 7.993e-17 1.543e-12 7.136e-12 0.698 0.8102 0.7173 0.6161 0.4988
fisheries 3.244e-08 0.03818 3.482e-05 3.537e-05 0.1503 1.082e-10 2.644e-09 4.256e-10 2.255e-12 3.529e-11 2.195e-11 7.658e-10 1.19e-10 1.498e-12 0.00106 0.05546 0.4747 0.02363 0.7923
cumulative_exposure 0.004133 0.758 0.008151 0.03024 0.3887 2.25e-05 0.002431 3.938e-05 5.742e-07 6.603e-08 3.066e-07 0.0005552 8.874e-06 6.075e-07 0.7661 0.6575 0.9673 0.5452 0.33

3. Relationships with benthic communities

3.1. Taxa identity

The most abundant taxa in our study area are:

  • Density: B.neotena (1969), E. integra (1158), P.grandimana (1092), Nematoda (1044) and M. calcarea (575)
  • Biomass: E. parma (biomass of 531.5), Strongylocentrotus sp. (65.3), N. incisa (58.5), M. calcarea (45.4) and S. groenlandicus (34.3)

The following graphs present the distribution of sampled phyla along index of cumulative exposure, according to density (left panel) or biomass (right).

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 1.384. Five exposure categories from ‘bad’ to ‘high’ have been set with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

Phylum mean density by group
Phylum low bad moderate high good
Annelida 15.7 33.1 14.5 37 30.1
Arthropoda 12.7 42.5 45.7 45 45.7
Cnidaria 0 0 0 0 0.0147
Echinodermata 0.333 0 11.2 1.08 3.57
Mollusca 16.3 5.56 5.83 22.7 14.6
Nematoda 0 0.389 0.167 7.92 13.7
Nemertea 0 0.222 0 0 0.176
Sipuncula 0.667 0.5 0.333 0.231 0.191
Phylum mean biomass by group
Phylum low bad moderate high good
Annelida 4.95 0.85 0.344 1.42 1.24
Arthropoda 0.0278 0.0663 0.0485 0.104 0.155
Cnidaria 0 0 0 0 0.0494
Echinodermata 0.00727 0 1.64 6.27 7.45
Mollusca 2.96 1.03 3.96 2.05 1.12
Nematoda 0 3.89e-05 1.67e-05 0.000669 0.000581
Nemertea 0 0.095 0 0 3.24e-05
Sipuncula 0.0281 0.00151 0.0665 0.00156 0.00831

3.2. Community characteristics

The following graphs present the distribution of community characteristics along index of cumulative exposure.

Exposure categories are based on the exposure index: the higher the index, the lower the status. Maximum cumulative exposure is 1.384. Five exposure categories from ‘bad’ to ‘high’ have been set with 20 %, 40 %, 60 % or 80 % of the maximum exposure.

By exposure gradient

By exposure categories

4. Regressions

4.1. Data manipulation

For the following analyses, independant variables are exposure indices, dependant variables are community characteristics. Variables have been standardized by mean and standard-deviation.

We added abiotic parameters to the independant variables, but this did not increas significantly predictive power.

All stations and predictors were selected for the regressions, as we are interested in each of them (following graphs are for information only).

Correlation coefficients between exposure indices
  aquaculture city dredging industry sewers shipping fisheries
aquaculture 1 -0.243 -0.09 -0.383 -0.168 -0.173 0.298
city -0.243 1 0.107 0.371 -0.165 -0.302 -0.111
dredging -0.09 0.107 1 0.452 0.217 0.034 -0.166
industry -0.383 0.371 0.452 1 0.452 -0.024 -0.353
sewers -0.168 -0.165 0.217 0.452 1 -0.045 -0.241
shipping -0.173 -0.302 0.034 -0.024 -0.045 1 -0.607
fisheries 0.298 -0.111 -0.166 -0.353 -0.241 -0.607 1

4.2. Univariate regressions

We used linear models for the regressions on community characteristics. Variables have been standardized by mean and standard-deviation (coefficients need to be back-transformed to be used in predictive models). Variable selection was not needed here, as we are interested in all exposure indices.

Results of regressions (coefficients with a significant p-value for marginal tests) are shown on the table below:

Predictor S N B H J
Depth + + + +
Aquaculture +
City
Dredging
Industry
Sewers
Shipping
Fisheries +
Adjusted \(R^{2}\) 0.21 0.02 0 0.3 0.15

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Richness
## FULL MODEL
## Adjusted R2 is: 0.21
Fitting linear model: S ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.229e-16 0.08573 -8.433e-15 1
depth 0.2609 0.09445 2.763 0.006837 * *
aquaculture 0.1741 0.08852 1.967 0.05194
city -0.007507 0.1087 -0.06904 0.9451
dredging -0.04464 0.09307 -0.4796 0.6325
industry -0.07245 0.1277 -0.5673 0.5718
sewers -0.1492 0.1253 -1.19 0.2367
shipping 0.06154 0.09536 0.6453 0.5202
fisheries 0.1887 0.09431 2.001 0.04816 *
## RMSE from cross-validation: 0.9148124
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.1 1.03 1.26 1.08 1.48 1.46 1.11 1.09

Density
## FULL MODEL
## Adjusted R2 is: 0.02
Fitting linear model: N ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.014e-16 0.09524 1.064e-15 1
depth -0.2617 0.1049 -2.495 0.01427 *
aquaculture -0.02021 0.09834 -0.2055 0.8376
city 0.02281 0.1208 0.1888 0.8506
dredging -0.1044 0.1034 -1.01 0.315
industry -0.1317 0.1419 -0.928 0.3556
sewers -0.0106 0.1392 -0.07618 0.9394
shipping -0.07147 0.1059 -0.6746 0.5015
fisheries 0.0705 0.1048 0.6729 0.5026
## RMSE from cross-validation: 1.018865
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.1 1.03 1.26 1.08 1.48 1.46 1.11 1.09

Biomass
## FULL MODEL
## Adjusted R2 is: -0.01
Fitting linear model: B ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.632e-16 0.09662 -7.899e-15 1
depth -0.1452 0.1064 -1.364 0.1757
aquaculture -0.09684 0.09976 -0.9707 0.3341
city -0.1979 0.1226 -1.615 0.1095
dredging -0.01567 0.1049 -0.1494 0.8815
industry 0.1532 0.1439 1.064 0.2898
sewers -0.2672 0.1412 -1.892 0.06145
shipping -0.191 0.1075 -1.777 0.07867
fisheries 0.01389 0.1063 0.1306 0.8963
## RMSE from cross-validation: 1.014689
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.1 1.03 1.26 1.08 1.48 1.46 1.11 1.09

Diversity
## FULL MODEL
## Adjusted R2 is: 0.3
Fitting linear model: H ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.751e-19 0.08077 3.406e-18 1
depth 0.5195 0.08899 5.838 6.72e-08 * * *
aquaculture 0.1249 0.0834 1.498 0.1374
city 0.0532 0.1025 0.5193 0.6047
dredging 0.07932 0.08769 0.9046 0.3679
industry -0.1007 0.1203 -0.837 0.4046
sewers -0.1083 0.1181 -0.9175 0.3611
shipping 0.02583 0.08985 0.2875 0.7743
fisheries -0.02838 0.08886 -0.3194 0.7501
## RMSE from cross-validation: 0.9270929
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.1 1.03 1.26 1.08 1.48 1.46 1.11 1.09

Evenness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: J ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.683e-18 0.08883 -4.146e-17 1
depth 0.4268 0.09787 4.361 3.171e-05 * * *
aquaculture 0.01178 0.09172 0.1284 0.8981
city 0.08589 0.1127 0.7623 0.4477
dredging 0.1304 0.09644 1.352 0.1795
industry -0.1648 0.1323 -1.245 0.216
sewers -0.003056 0.1299 -0.02353 0.9813
shipping -0.04358 0.09881 -0.4411 0.6601
fisheries -0.1774 0.09772 -1.815 0.07251
## RMSE from cross-validation: 1.095543
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.1 1.03 1.26 1.08 1.48 1.46 1.11 1.09

Annelida density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.14
Fitting generalized (poisson/log) linear model: annelids ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.313 0.01927 171.9 0 * * *
depth -0.3414 0.02211 -15.44 8.702e-54 * * *
aquaculture 0.09114 0.01448 6.295 3.082e-10 * * *
city 0.0784 0.0203 3.863 0.0001122 * * *
dredging -0.1111 0.02663 -4.172 3.015e-05 * * *
industry -0.1376 0.0302 -4.557 5.182e-06 * * *
sewers -0.1168 0.0306 -3.818 0.0001345 * * *
shipping 0.05565 0.01803 3.087 0.002023 * *
fisheries -0.08549 0.0228 -3.75 0.0001768 * * *
## Unbiased RMSE from cross-validation: 35.999
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.1 1.04 1.25 1.08 1.39 1.32 1.11 1.09

Arthropoda density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.12
Fitting generalized (poisson/log) linear model: arthropods ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.673 0.0164 224 0 * * *
depth -0.2892 0.01753 -16.5 3.655e-61 * * *
aquaculture -0.1632 0.02192 -7.445 9.658e-14 * * *
city 0.09516 0.01788 5.322 1.024e-07 * * *
dredging -0.2565 0.02799 -9.163 5.049e-20 * * *
industry -0.3603 0.03038 -11.86 1.95e-32 * * *
sewers 0.2588 0.02228 11.61 3.536e-31 * * *
shipping 0.01662 0.01531 1.085 0.2777
fisheries -0.02626 0.01659 -1.583 0.1135
## Unbiased RMSE from cross-validation: 92.58253
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.13 1.02 1.22 1.04 1.78 1.83 1.09 1.08

Mollusca density
## FULL MODEL
## McFadden's pseudo-R2 is: 0.16
Fitting generalized (poisson/log) linear model: molluscs ~ depth + aquaculture + city + dredging + industry + sewers + shipping + fisheries
  Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.489 0.02971 83.79 0 * * *
depth 0.03342 0.02732 1.223 0.2213
aquaculture 0.112 0.02004 5.586 2.324e-08 * * *
city 0.07535 0.0278 2.711 0.006713 * *
dredging -0.104 0.03525 -2.95 0.003179 * *
industry 0.2065 0.0293 7.047 1.828e-12 * * *
sewers -0.3625 0.04791 -7.566 3.835e-14 * * *
shipping -0.3105 0.04152 -7.479 7.502e-14 * * *
fisheries 0.1305 0.02228 5.855 4.762e-09 * * *
## Unbiased RMSE from cross-validation: 18.9517
Variance Inflation Factors
  depth aquaculture city dredging industry sewers shipping fisheries
VIF 1.08 1.04 1.29 1.12 1.28 1.16 1.06 1.08

4.3. Multivariate regression

The model selected by the DistLM procedure has a \(R^{2}\) of 0.22. Colours represent the value of the cumulative exposure index (the bluer, the higher).


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